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In this paper, we present a distributed model predictive control (DMPC)scheme for dynamically decoupled systems which are subject to stateconstraints, coupling state constraints and input constraints. In the proposedcontrol scheme, neighbor-to-neighbor communication suffices and all subsystemssolve their local optimization problem in parallel. The approach relies onconsistency constraints which define a neighborhood around each subsystem'sreference trajectory where the state of the respective subsystem is guaranteedto stay in. Reference trajectories and consistency constraints are known toneighboring subsystems. Contrary to other relevant approaches, the referencetrajectories are improved iteratively. Besides, the presented approach allowsthe formulation of convex optimization problems even in the presence ofnon-convex state constraints. The algorithm's effectiveness is demonstratedwith a simulation.

A Consistency Constraint-Based Approach to Coupled State Constraints in Distributed Model Predictive Control